Currently, there are no adequate solutions to the problem of tracking the 3D-pose of small, resource-constrained systems in unknown environments. Specifically, estimating the motion of miniature devices, similar in size to a mobile phone, is difficult. In contrast to medium and large-scale systems, (e.g. mobile robots, UAVs, autonomous cars), small devices have limited computational capabilities and battery life, factors which make the pose-estimation problem challenging. In the absence of GPS, the types of sensors that can be used for pose estimation in small-scale systems are quite restricted.
A key challenge in designing an estimator for this task is the limited availability of processing power. Feature detection algorithms can track hundreds of features in images of natural scenes, but processing all this data in real time is challenging, particularly for a small, resource-constrained device.
Additional difficulties arise when designing an estimator for any localization task. The computational efficiency of different methods depends strongly on the nature of each particular dataset. For instance, one algorithm may outperform all others when the environment is feature-rich and the vehicle is moving slowly, while a different algorithm may be the fastest in feature-sparse environments under fast motion. This makes algorithm selection a difficult task, for which no general, systematic methods exist to date.
A huge number of methods have been proposed for pose estimation, however, existing methods typically consist of a single approach for processing the feature measurements (e.g., EKF-based SLAM, or sliding-window iterative minimization). As a result, they often do not generalize well to different environments, and are difficult to adapt to the varying availability of computational resources.
Therefore there is a need for a hybrid-estimator system using visual and inertial sensors for real-time pose tracking on devices with limited processing power.